Urban Land Development for Industrial and Commercial Use - MDPI

sustainability Article

Urban Land Development for Industrial and Commercial Use: A Case Study of Beijing Chuanzhun Sun 1,† , Chao Sun 2,† , Zhenshan Yang 3, *, Jikang Zhang 4 and Yu Deng 3 1 2 3

4

* †

College of Public Management, South China Agricultural University, No. 483, Wushan Road, Tianhe District, Guangzhou 510642, China; [email protected] Department of Engineering Physics, Institute of Public Safety Research, Tsinghua University, Zhongguancun North Street, Haidian District, Beijing 100084, China; [email protected] Key Lab of Regional Sustainable Development and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Bejing 100101, China; [email protected] (Z.Y.); [email protected] (Y.D.) Shenzhen Urban Planning & Land Resource Research Center, 8009 Hongli Road, Futian District, Shenzhen 518040, China; [email protected] Correspondence: [email protected]; Tel.: +86-10-6488-9035 These authors contribute equally to this paper.

Academic Editor: Marc A. Rosen Received: 2 November 2016; Accepted: 9 December 2016; Published: 15 December 2016

Abstract: Since the 20th century, urbanization has been the main characteristic of global land development. If we can reveal and understand the characteristics and underlying mechanisms of urban development, we can then identify a sustainable development pattern for cities. In this paper, we primarily focus on the determinants of two main types of land use in urban development, industrial and commercial, in an empirical study of Beijing. We use a spatial data analysis method to seek and model major determinants of industrial and commercial land growth in the period of 2000–2010 in Beijing. A spatial logistic regression model is used to explore the impact of spatial independent variables on these two types of land use. The study shows that: (1) newly-added industrial land during 2000–2010 received significant contributions from the number of local enterprises engaged in services in 2010, the use of land for agriculture and construction in the neighborhood in 2000 and planning orders; (2) factors contributing to land transferred for commercial use included the number of enterprises, construction land in the neighborhood and accessibility improvement. Keywords: urban development; industrial land; commercial land; growth; Beijing

1. Introduction Determinants of land use change are important for understanding and formulating urban development policy. These determinants also guide analysts and planners in making suitable land development strategies. Often, changes in land use result in complex interactions of various landscape functions [1], affecting social and economic activities. Therefore, it is expected that different land use changes occur because of different processes of development and are determined by different factors. However, this is not well addressed in current land use studies. In this paper, we primarily focus on the determinants of two main types of land use in urban development, industrial and commercial, in an empirical study of Beijing. Various models and approaches have been developed to examine urban land use change. From institutional and political economy perspectives, urban land use change is viewed as an institutional process [2] that, particularly in China, is driven by reform and globalization and led by the state, Sustainability 2016, 8, 1323; doi:10.3390/su8121323

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state-centered development alliances and multinational enterprises [3–5]. With the development of geographic information systems (GIS), remote sensing and spatial statistics, various models are used to analyze and simulate land use or urban growth patterns [6–8], including in the context of China [9,10], which allows for examining the extent of specific factors. Land-centered development has aroused wide discussion as researchers attempt to understand it within the context of socioeconomic development. For instance, Deng et al. [11] found that economic growth is of overwhelming importance to the determination of urban land use. Zhang [12] applied the concept of a growth machine to understand the growth of local economies in China. The results showed that individual land-related local interest groups are emerging and economic coalitions exist to different degrees in Suzhou and Shenzhen. Jiang et al. [13] identified population urbanization and industrialization, as well as economic restructuring and growth as the demand forces underlying urban land use expansion. It is now widely believed that land use change is determined by multiple socioeconomic, physical and institutional factors. However, this analysis usually embraces a dichotomous view of urban land use, i.e., construction or non-construction, and usually does not go as far as to provide a detailed structure of the construction land use. In practice, manufacturing and commercial uses are two main types of urban land use, which vary under different courses. Verburg et al. [14] found that expansions of industrial or commercial land were driven by a combination of transportation accessibility, spatial policies and neighborhood interactions. Though there is a significant implication for practice, the land use change under different functions is examined to a lesser extent. In this paper, we hypothesize that industrial and commercial land uses have different determinants. These determinants are examined using a logistic regression model for urban land use change in Beijing during 2000–2010. We assess the relationship of manufacturing and commercial land use changes with locational accessibility, social and economic factors, built environment, natural factors and institutional factors; therefore, in this paper, industrial land and commercial land use are dependent variables. We use the logistic model to explore the influence of various independent variables on these two types of land use. Additionally, compared with other relative studies, transportation accessibility is the main reference factor in our study, and it includes 18 indicators, such as distance to the city center, distance to the employment center and distance to a subway station. Moreover, we take the dynamic change of these indicators into account. Existing research considers only a single transport type that impacts city land use [15–18], whereas we analyze several. Furthermore, transport accessibility was measured in terms of distance, time and transport cost, which reflect all of these aspects to be somehow less [19,20]. 2. Methods and Data Beijing is China’s capital city, with a population of 19.6 million by the end of 2010 and 21.5 million in 2014 [21]. It is one of the cities witnessing the fastest urban growth in China and perhaps in the world. Urban land use is also undergoing dramatic change and in many cases out-pacing planned scenarios, in both spatial expansion and function. This rapid development is exemplified by the building up of eight commercial centers and industrial bases, including Zhongguancun High-tech Park, Olympic Park Commercial Center and Shunyi Modern Manufacturing Base. The city area is continuously extending, and the city function is likewise continuously improving. The improvement mainly features the stripping and disintegration of the city function, namely extracting the non-core function, which is not related to the core function, from the city gradually, which is mainly to alleviate the current disparities among population, resource, environment and development in Beijing, and promoting the sustainable development of the city. For example, in order to implement the strategy of coordinated development of Beijing, Tianjin and Hebei and meanwhile stripping the non-core city function, in May 2016, the central policy makers formally established a strategy of the construction of the “city sub-center of Beijing” in the suburban region of Tongzhou district. To put that strategy into practice, a large number of functional entities (such as the administrative


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entity, commercial entity, medical entity and education educational entity) will be gradually relocated to Tongzhou. Tongzhou New Town will become a comprehensive sub-center with administrative, cultural, economic and educational functions. In this situation, the determinants of urban land use become a critical concern of urban land development. 2.1. Choice of Independent Variables 2.1.1. Social and Economic Factors Urban land use change is driven by social and economic development. Population density is often regarded as an important factor in determining land use, by indicating labor availability, accessibility or the presence of local markets [22,23]. Together with population, the size and distribution of enterprises to a large extent reflect agglomeration economies, which is an important driver of modern cities’ land use change, especially in Beijing [24–26]. Three population factors (population in 2000, population in 2010 and the population growth rate during 2000–2010) and nine enterprise factors (number of enterprises, service enterprises and industrial enterprises in 2000 and 2010, as well as the fluctuation in that number during 2000–2010) were selected to reflect urban land use change. 2.1.2. Built Environment The built environment, which is largely decided by existing land use, provides a main area for living, working and playing on a day-to-day basis [27]. Among others, location, particularly accessibility, significantly influences economic and human activities [28,29], and existing land use in neighborhoods is expected to guide the function of land use changes [30–32]. The built environment can be further classified as transportation accessibility and neighborhood land use. (a) Transportation accessibility plays an important role in shaping the spatial structure of the city, which also creates and shapes economic and social opportunities [33,34]. A transportation accessibility indicator is usually considered to be the main factor driving city expansion, and it is widely used in studies of forces driving such expansion [35]. In this paper, we consider the synthesis of transport accessibility from six aspects, city center accessibility, central business district (CBD) accessibility, employment center accessibility, industrial center accessibility, subway center accessibility and high speed way accessibility, covered by 18 indicators (please see Table 1). Table 1. Characteristics of independent variables. Name of Variable

Type

Unit

Max a

Min b

Mean

SD c

Population density (2010) Number of service enterprises (2010) Growth of service enterprises (2000–2010) Number of industrial enterprises (2010) Growth of industrial enterprises (2000–2010) Static accessibility principal factor Dynamic accessibility principal factor Construction land in neighborhood Forest land in neighborhood Agriculture land in neighborhood Water area in neighborhood Urban planning control DEM

Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Binary Continuous

Person/sq. km Per sq. km Per sq. km Per sq. km Per sq. km Minutes Minutes % % % % – Meter

359,400 8683 2.01 1461 0.91 539 213 100 100 100 100 1 1234

5000 82 −0.05 10 −0.6 13 0 0 0 0 0 0 −126

89,122 861 0.83 304 0.18 197 46 41.61 10.94 32.78 6.23

57,023 766 0.34 240 0.21 94 36 32.36 18.45 27.96 12.68

62

100

a

Max: maximum; b Min: minimum; c SD: standard deviation.

(b) Land use in adjacent areas accounts for the possible effects of spatial interaction on land use decisions [30,31]. New construction is path dependent, which means that if the proportion of construction land use around some areas is larger, the probability of there being new construction land use in these regions is perhaps higher. The possibility of urbanization is most closely related to its neighborhood [9]. Construction could, however, be deterred by non-construction uses in


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the neighborhood, such as water and agricultural uses, particularly under circumstances similar to those in China where there is strong legislative protection of agricultural land. Thus, the role of neighborhood uses in affecting industrial and commercial land uses was discriminated into two types: promoting factors if the neighborhood is dominated by construction or restrictive factors if occupied by agricultural or forest land and water areas. When we choose the neighborhood independent variable, we take the percentage of construction land, agricultural land, forest land and bodies of water into account. It is generally acknowledged that higher construction land usage will promote land development. Meanwhile, because they are protected by land use policy, a higher percentage of agricultural and forest land will decrease the possibility of land development. However, agricultural land is always located in a plain land region, and its development cost is relatively low, which increases the possibilities for occupation, even if it is being protected. 2.1.3. Natural Factors Natural factors, especially slope, dramatically influence population distribution, the cost of land development and the function of land use [36]. Therefore, the digital elevation model (DEM) as the topographic factor is involved in the scope of the independent variable. 2.1.4. Institutional Factors The evolution of urban spatial structure is heavily influenced by spatial policies as they encourage or limit certain kinds of land use [9]. However, quantitative impacts of spatial policies on land use change are hard to assess [37]. A land use plan was used to indicate policies controlling and guiding new commercial and industrial land. 2.2. Data Preparation and Processing 2.2.1. Dependent Variables Detailed land use data from 2000 and 2010, provided by Beijing Land Use Bureau (BLUB) at a spatial scale of 1:10,000, was used to measure the functional change of urban land over one of the fastest growth periods in Beijing. In 2000 and 2010, BLUB carried out a detailed land use survey of Beijing. Land use types in 2000 and 2010 included construction, agriculture, forest and bodies of water. However, owing to a change in land use classification standards, in 2010, construction land was divided into commercial and industrial land and others. Therefore, using spatial overlay analysis, we get the newly-defined commercial and industrial land uses during 2000–2010. The modified uses were mainly agriculture-related types and therefore did not affect our analysis. Industrial land (for manufacturing) and commercial land (for business and services activities, not including residential ones, which were another specific type of land use in urban planning in China) were extracted using GIS software and were dependent variables in this study. As most specified land use occurred in and around the urban center, the analysis was confined to the main urban districts in Beijing (Figure 1). In this study, we chose the region within the 6th Ring Road as the study area, which included the Core Zone of Capital Function (CZCF), the Urban Function Extended Zone (UFEZ), the New Zone of Urban Development (NZUD) and the Eco-conservation Zone (ECZ) (Figure 1).

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Figure 1. Study area: Core Zone of Capital Function (CZCF), the Urban Function Extended Zone Figure Studyarea: area: Core Core Zone Zone of Capital (CZCF), the Extended Zone Figure 1.1.Study Capital Function Function (CZCF), theUrban UrbanFunction Function Extended Zone (UFEZ), the New Zone of UrbanofDevelopment (NZUD) and the Eco-conservation Zone (ECZ). (UFEZ), New Zone of Urban Development (NZUD) Eco-conservation Zone (ECZ). (UFEZ), thethe New Zone of Urban Development (NZUD) andand the the Eco-conservation Zone (ECZ).

2.2.2. Independent Variables 2.2.2. 2.2.2. Independent Independent Variables Variables (a) Transportation accessibility indicator data: (a) accessibility indicator indicator data: data: (a) Transportation Transportation accessibility We collected transport data on different road ranges; these spatial data come from the Beijing We collected transport data different road these data come the Wetransport collected transport data on onwhich different road ranges; ranges; these spatial spatial datatraffic, come from from the Beijing Beijing master map (2004–2020), includes the expressway, arterial secondary main master transport map (2004–2020), which includes the expressway, arterial traffic, secondary main masterbranch transport map (2004–2020), includes the expressway, arterial secondary roads, roads and urban masswhich transit. We gained the average speed oftraffic, each level road inmain 2000 roads, branch roads and urban mass transit. We gained the average speed of each level road in 2000 roads, branch roads and urban mass transit. We gained the average speed of each level road in 2000 and 2010 by referring to existing literature [38] and reports [39]. Then, we used network analysis and and referring totoexisting literature [38] and reports [39]. Then, we used network analysis and and 2010 2010by by referring existing [38] and reports [39]. Then, we used network weighted distance analysis [19,34]literature on the ArcGIS platform to generate time maps (100-manalysis spatial weighted distance analysis [19,34] on the ArcGIS platform to generate time maps (100-m spatial and weighted distance analysis [19,34] on to thethe ArcGIS platform to generate time maps (100-m resolution) encompassing the study area city center, CBD, industrial center and somespatial other resolution) encompassing the study area to the city center, CBD, industrial center and some other resolution) encompassing the study area to the city center, CBD, industrial center and some other elements. All of these maps can be considered transportation accessibility maps, and through overlay elements. All of these maps can be considered transportation accessibility maps, and through overlay elements.ofAll of2000 theseand maps canmaps, be considered transportation accessibility maps, and through overlay analysis the 2010 we acquired the dynamic change transportation accessibility analysis of the the2000 2000and and2010 2010maps, maps, acquired dynamic change transportation accessibility analysis of wewe acquired thethe dynamic change transportation accessibility map. map. map. eliminate the collinear relation among different indicators, we adopted a principal component To eliminate the collinear relation among different indicators, we adopted a principal To method eliminate the collinear relation among indicators, we adopted of a our principal analysis to obtain thetofirst principal factor ofdifferent the factor static and dynamic accessibility study component analysis method obtain the first principal of the static and dynamic accessibility component analysis method to obtain the first principal factor of the static and dynamic accessibility area. The first principal factors’ variance explanations are 84.07% and 83.75%, respectively (Figure 2). of our study area. The first principal factors’ variance explanations are 84.07% and 83.75%, of our study area. The first principal factors’ variance explanations are 84.07% and 83.75%, The static accessibility factor revealedprincipal the pattern of monocentric allocation respectively (Figure 2).principal The static accessibility factor revealed thetransportation pattern of monocentric respectively (Figure The static accessibility principal factor revealed the pattern of monocentric in Beijing, while the 2). dynamic accessibility factor revealed theprincipal strength of improvement of transportation allocation in Beijing, whileprincipal the dynamic accessibility factor revealed the transportation allocation in Beijing, while thearea. dynamic accessibility principal factor revealed the transportation in different regions in the study strength of improvement of transportation in different regions in the study area. strength of improvement of transportation in different regions in the study area.

(a) (a)

(b) (b)

Figure Transportation accessibility principal map. Static accessibility; dynamic Figure 2.2.Transportation accessibility principal factor factor map. (a) Static(a) accessibility; (b) dynamic(b) accessibility. Figure 2. Transportation accessibility principal factor map. (a) Static accessibility; (b) dynamic accessibility. accessibility.


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(b)(b)Population Populationindicator indicatordata: data: We used data from the the fifthfifth and and sixthsixth population censuses in Beijing to get the permanent resident We used data from population censuses in Beijing to get the permanent population data of every town in Beijing in the years 2000 and 2010. Then, we used inverse distance resident population data of every town in Beijing in the years 2000 and 2010. Then, we used inverse weighting (IDW) interpolation to shapetothe population grid map resolution) in 2000 and distance weighting (IDW) interpolation shape the population grid (100-m map (100-m resolution) in 2000 2010, well to as denote the change between the the twotwo years. However, to to decrease the and as 2010, asas well to denote the change between years. However, decrease thecollinearity collinearityof different population indicators, which we will into the model, we make an effort to reduce of different population indicators, which we input will input intologistic the logistic model, we make an effort to the dimensionality of the indicators to guarantee the feasibility and stability this model. we reduce the dimensionality of the indicators to guarantee the feasibility and of stability of thisWhen model. performed analysis of analysis all of theofpopulation indicators,indicators, we found we thatfound there that was there significant When we correlation performed correlation all of the population was correlation the different indicators (Table 2). The correlation ofcoefficient the population significantamong correlation among the different indicators (Table 2). Thecoefficient correlation of thein population inin 2010 with 2000 and the population change were 0.5 and 0.6,thus, respectively; thus, 2010 with that 2000 andthat the in population change were 0.5 and 0.6, respectively; the population population appeared to be thetobest indicator represent indicators, all population andinto it inthe 2010 appearedinto2010 be the best indicator represent all to population andindicators, it was input was input into the logistic model. As shown in Figure 3, the average population density of the study the logistic model. As shown in Figure 3, the average population density of the study area in 2010 was 2 . The area in 2010 89,122 perdensity km2. The higher areaaround was distributed around the city center. 89,122 per kmwas higher area was density distributed the city center. Table results. Table2.2.Parameters Parameters of of the model results. Percentage

−2 Log

Hosmer–Lemeshow Percentage Correct Correct Hosmer–Lemeshow −2 Log Likelihood Likelihood Commercial land 70.30% 70.30% Commercial land Industrial land Industrial land 65.60% 65.60%

0.286 0.286 0.216 0.216

2260.423 2260.423 12,263.909 12,263.909

Cox and Snell

Cox and Snell R Square R Square

0.202 0.202 0.141 0.141

Nagelkerke

Nagelkerke R Square R Square

0.269 0.269 0.189 0.189

Figure 3. Population density in 2010. Figure 3. Population density in 2010.

(c) Economic and employment data: (c) Economic and employment data: Using data from the first and second economic census in Beijing, we obtained the total number Using data from the first and second economic enterprises census in Beijing, we obtained number of all enterprises, service enterprises and industrial of every town, whichthe is total the smallest ofadministrative all enterprises,unit service enterprises and industrial enterprises of every town, which is the smallest in China. Then, we used IDW spatial interpolation to shape the number of administrative unit in China. Then,resolution) we used IDW spatial interpolation to shape the number enterprises grid map (100 m spatial in 2000 and 2010 and the change between the twoof enterprises grid map (100 m spatial resolution) in 2000 and 2010 and the change between the two years. years. As with the population indicator data, we reduced the dimensionality of the indicators using Ascorrelation with the population indicator data, we reduced the dimensionality of the indicators using correlation analysis (Table 3). Finally, we chose the number of service enterprises (2010), growth of analysis (Table 3). Finally, we chose the of number of service enterprises (2010), growth of service service enterprises (2000–2010), number industrial enterprises (2010) and growth of industrial enterprises (2000–2010), number of industrial enterprises (2010) and growth of industrial enterprises enterprises (2000–2010) as the elements to input into the logistic model. (2000–2010) as the to input intoof the logistic As shown byelements the spatial grid map the abovemodel. indicators, the spatial agglomeration degree of As shown by the spatial grid map above indicators, the spatial agglomeration industrial density is relatively weak in of thethe city center in 2010 (Figure 4). However, arounddegree the cityof industrial density is relatively weak in the city center in 2010 (Figure 4). However, around the city center, we can identify an increasing trend in industrial density during 2000–2010. Meanwhile,center, the we can identify an increasing in industrial density during 2000–2010. spatial spatial agglomeration degreetrend of service enterprises is significant in the core Meanwhile, area and thethe function agglomeration degree of service enterprises is significant in the core areatrended and the toward functionextending extension extension area. During 2000–2010, the activities of service enterprises northwards in Changping southwards in Daxing. area. During 2000–2010, theand activities of service enterprises trended toward extending northwards in Changping and southwards in Daxing.


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Figure 4. Economic and employment 2000–2010. Figure 4. Economic and employmentdistribution distributionin in2010 2010 and and during 2000–2010. Table 3. Results logistic regressionofofnew newcommercial commercialand and industrial industrial land. Table 3. Results of of logistic regression land. Variables Variables Population density (2010) Number of service enterprises Population density (2010) (2010) Growth of service enterprises Number of service enterprises (2010) Number enterprises (2010) Growthofofindustrial service enterprises Growth of of industrial industrial enterprises Number enterprises(2000–2010) (2010) Accessibility (2010) Growth of industrial enterprises (2000–2010) Change of accessibility (2000–2010) Accessibility (2010) Neighborhood construction land Change of accessibility (2000–2010) Neighborhood agricultureland land Neighborhood construction Neighborhood forest land Neighborhood agriculture land Neighborhood forestwaters land Neighborhood Neighborhood waters Urban planning Urban planning DEM DEMratio statistic Likelihood: Likelihood: ratio statistic Number in sample Number in sample

β

β 0.001 7.55 0.001 7.55-–−0.98 −1.234 −0.98 −1.925 −1.234 0.664 −1.925 0.931 0.664 0.931-– -– -– -– −11.928 −11.928 2260 2260 1000 1000

Commercial Land Commercial Land Odds Ratio Wald p-Value χ2 2 expRatio (β) Odds p-Value Wald χ exp (β) 0.029 4.762 1.00 72.169 1900.74 0.0290 4.762 1.00 --00.293 72.169 1900.74 0.006 7.545 0.38 0.293 – – 0.002 9.938 0.29 0.006 7.545 0.38 17.322 0.15 0.0020 9.938 0.29 2.914 1.94 00.088 17.322 0.15 18.318 2.54 0.0880 2.914 1.94 --00.383 18.318 2.54 0.866 0.383 – -–-0.866 – -–-0.296 0.296 – -–-0.129 0.1290 – – 31.799 0.00 0 31.799 0.00

Industrial Land Industrial Land Odds Ratio Wald p-Value χ2 2 expRatio (β) Odds p-Value Wald χ exp (β) 0 38.306 0.30 13.245 3.05 00 38.306 0.30 0.699 --0 13.245 3.05 0.507 0.699 –-–-0.037 4.369 1.69 – 0.507 – – −2.134 0 108.594 0.12 0.037 4.369 1.69 -0.341 --−2.134 0 108.594 0.12 0.767 0.001 11.372 2.15 – 0.341 – – 0.408 0.067 3.364 1.50 0.767 0.001 11.372 2.15 −0.566 0.03 4.699 0.57 0.408 0.067 3.364 1.50 −−0.617 0.566 0.03 4.699 0.57 0.02 5.447 0.54 −0.437 0.617 0.020 5.447 0.54 55.541 1.55 0.437 00 55.541 1.55 −12.106 117.787 0.00 −12,263 12.106 0 117.787 0.00 12,263 1000 1000 β β −1.212 −1.116 1.212 -1.116 –--

(d) Neighborhood data: (d) Neighborhood data: Based on the land use data in 2000, we take the percentages of construction, agriculture and forest andland bodies water within 1 take km2 as measurements to shape theagriculture neighborhood Basedland on the useofdata in 2000, we thethe percentages of construction, and land forest 2 map (100-m spatialwithin resolution; As shown by to these maps, is a high percentage of landuse and bodies of water 1 kmFigure as the5). measurements shape thethere neighborhood land use map construction land in the core area and the function extension area. The distribution of the construction (100-m spatial resolution; Figure 5). As shown by these maps, there is a high percentage of construction expanded in the vicinity of the main road. The land is mainly in land the landland in the core area and the function extension area.agricultural The distribution of the distributed construction southeast partvicinity of the study theThe forest land is mainly in the west.inThe of expanded in the of thearea, mainand road. agricultural land distributed is mainly distributed thebodies southeast

part of the study area, and the forest land is mainly distributed in the west. The bodies of water are

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water Chaobai River River in inthe theeast, east,the theLiang LiangRiver Riverininthe the waterare arethe theSha Shaand andQing Qingrivers rivers in in the the north, north, the Chaobai the Sha and Qing rivers in the north, the Chaobai River in the east, the Liang River in the south and south and the Yongding River in the west. In addition, there is a high percentage of water area in the south and the Yongding River in the west. there is a high percentage of water area in the the Yongding River in the west. In addition, there is a high percentage of water area in the inner city. inner innercity. city.

Figure 5. Neighborhood land distribution. Figure 5. Neighborhood land distribution. Figure 5. Neighborhood land distribution.

(e) DEM and urban planning data: (e) (e)DEM DEMand andurban urbanplanning planningdata: data: We used 90-m resolution data provided by the Resources and Environment Data Center of The We 90-m resolution data provided Resources Environment Data of Weused used 90-m resolution byby the Resources andand Environment Data Center The Chinese Academy of Sciences.data Theprovided topography inthe Beijing is high in the northwest and lowCenter inofthe The Chinese Academy of Sciences. topography in Beijing is high in northwest theThe northwest and in low in Chinese Academy of Sciences. The The topography Beijing is high in the anddifference low the southeast, comprising a mountainous region andin alluvial plain, respectively. relative the comprising a mountainous region and alluvial plain,respectively. respectively.The Therelative relativedifference difference insoutheast, height between the ahighest and lowest points isalluvial 2295 m plain, (Figure 6). southeast, comprising mountainous region and ininheight heightbetween betweenthe thehighest highestand andlowest lowestpoints points is is 2295 2295 m m (Figure (Figure 6).

Figure 6. Topography in Beijing.

Figure Figure 6. 6. Topography Topography in in Beijing. Beijing.

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The Beijing Beijing City CityMaster MasterPlan Plan(2004–2020) (2004–2020) divided Beijing construction and [40][40] hadhad divided Beijing into into construction and nonnon-construction areas (Figure 7). Official urban planning maps of Beijing (2004–2020) were collected construction areas (Figure 7). Official urban planning maps of Beijing (2004–2020) were collected and and vectorized to measure the influence of spatial policy, the introduction of dummy variables vectorized to measure the influence of spatial policy, withwith the introduction of dummy variables one one the actual use was consistent planned or zero otherwise. if theif actual landland use was consistent withwith thatthat planned or zero otherwise.

Figure 7. construction and and non-construction non-construction areas Figure 7. construction areas in in Beijing. Beijing.

2.3. Spatial Sampling It is impossible to use all data in the the space space to to estimate estimate the the model, model, because of spatial autocorrelations with different spatial data [41]. Therefore, spatial sampling was necessary. In this study, input arguments arguments of of the thelogistic logisticmodel modelwere wereadopted adoptedtotostrictly strictly eliminate autocorrelation eliminate thethe autocorrelation of of sample points space; otherwise, stability validity of parameter estimations would sample points in in space; otherwise, thethe stability andand validity of parameter estimations would be be affected [30]. general, spatialsampling samplingstrategies strategiesinclude includerandom random sampling sampling and and systematic affected [30]. In In general, spatial sampling. Random Randomsampling samplingisisa amethod methodthat thatensures ensures that each space overall sample that each space in in thethe overall sample hashas an an equal chance of selection, it lacks reliability in terms reducing spatial autocorrelation equal chance of selection, butbut it lacks reliability in terms of of reducing thethe spatial autocorrelation of of samples [42]. Systematic sampling is also known as interval sampling. It is a sampling method in samples [42]. Systematic sampling is also known as interval sampling. It is a sampling which the total units are arranged in a certain order [9]. It is able to overcome the issues of sample spatial autocorrelation, autocorrelation, but but it loses some information for specific spatial points; thus, spatial sampling does not representation [9].[9]. In this study, a combination of theseoftwo strategies was adopted. notreflect reflectfull full representation In this study, a combination these two strategies was Systematic or intervalorsampling was used, was afterused, the starting point was point randomly determined and a unit adopted. Systematic interval sampling after the starting was randomly determined was in selected a regularininterval. and selected a unit was a regular interval. Specifically, using using the thegrid gridlayers layersofofthe the 100-m resolution environment, a starting 100-m resolution GISGIS environment, a starting pointpoint was was created randomly, the value the layers of both independent and dependent variables created randomly, then then the value of theoflayers of both independent and dependent variables were were sampled in 300-m intervals. More 5000 grids newcommercial commercialand andindustrial industrial land land were sampled in 300-m intervals. More thanthan 5000 grids of of new included in in this thisstudy studyarea. area.We Wedetermined determined that a sample greater than required that a sample sizesize greater than 400 400 waswas required [43].[43]. For For easy operation, we used 1000 samples in the modelling. Figure 8 shows a sample grid from this easy operation, we used 1000 samples in the modelling. Figure 8 shows a sample grid from this study. study. Note that although we omitted theuse land usefrom datathe from thearea coreinarea inthis 2010, this(the area (the white Note that although we omitted the land data core 2010, area white space space in Figure is a up built upthat arealacks that lacks new construction and therefore was therefore excluded in Figure 8) is a8) built area new construction land land and was excluded fromfrom our our analysis. analysis.

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Figure 8. Spatial sampling of commercial and industrial land. Figure 8. Spatial sampling of commercial and industrial land.

2.4. Logistic Model to Measure Land Use Change 2.4. Logistic Model to Measure Land Use Change A logistic model was used to estimate the relationship between industrial and commercial land A logistic model was used to estimate the relationship between industrial and commercial land use changes and the wide range of socioeconomic, built environment, nature and institutional factors. use changes and the wide range of socioeconomic, built environment, nature and institutional factors. This model is widely used to explore the main factors that could affect urban development and its This model is widely used to explore the main factors that could affect urban development and its driving forces [9,34]. Specifically, assuming that X is the response variable and P is the probability of driving forces [9,34]. Specifically, assuming that X is the response variable and P is the probability of the model, the corresponding regression model should be as follows: the model, the corresponding regression model should be as follows: k p1 ln p = α + ∑ βk Xki ln 1 − p1 = α k=1 β X

1−p

In this formula, p1 = P((yi = 1 |X 1i , X2i . . . , Xki ), as the values of variables X1i , X2i . . . , Xki are determined; p1 is thepprobability of 1 anXevent; α is an intercept; and β is a slope. The probability of In this formula, = P( yi = 1i , X2i … , Xki , as the values of variables X1i , X2i … , Xki 1 an event that occurs nonlinear function explanatory and the is are determined; p1 is is athe probability of an formed event; αby is the an intercept; andvariable β is a slope. Theexpression probability as follows: of an event that occurs is a nonlinear function by2 + the. .explanatory exp X1 + β2 X . + βn Xn ) variable and the expression (α + β1formed P= (1) is as follows: 1 + exp(α + β1 X1 + β2 X2 + . . . + βn Xn )

exp explain (α β 1 X β2regression X2 ⋯ βcoefficients X) Odds ratios are used to logistic of various independent 1 n n P= (1) variables [44]. In logistic regression, an odds ratio is commonly used to understand the effect of 1 exp (α β1 X1 β2 X2 ⋯ βn Xn ) an independent variable on the probability of event occurrence [45], which can be expressed by Odds ratios are used to explain logistic regression coefficients of various independent the following equation: variables [44]. In logistic regression, an odds ratio is commonly used to understand the effect of an independent variable on the can be expressed by the oddprobability α +event β1 X1occurrence + β2 X2 + . .[45], . + βwhich (2) (p) = exp(of n Xn ) following equation: Using binary logistic models in the SPSS tools, we calculated the regression coefficients, (2) odd(p) = exp (α β X1 β2 X2coefficient ⋯ βnestimation, Xn ) standard error (SE), the Wald χ2 statistic of1regression the significance level Using binarycoefficient logistic models in the we calculated the regression (P) of regression estimation andSPSS the tools, odds ratio (OR). Positive regression coefficients, coefficients standard error (SE),explanatory the Wald χ2 statisticincrease of regression significance level indicate that if the variables by an coefficient additional estimation, unit value, the odds ratio would (P) of regression coefficient estimation andregression the oddscoefficients ratio (OR).indicate Positive regression coefficients increase accordingly. Conversely, a negative that the odds ratio would indicate the the explanatory variables increase bytoantest additional value, odds ratio would decrease.that We ifused Hosmer–Lemeshow (HL) test goodnessunit of fit indexthe of the logistic model. increase accordingly. Conversely, a negative regression coefficients indicate that the odds ratio would decrease. We used the Hosmer–Lemeshow (HL) test to test goodness of fit index of the logistic model. The significance of the HL index indicates poor model fitting, while its non-significance indicates perfect model fitting. HL is calculated as follows [46]:


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The significance of the HL index indicates poor model fitting, while its non-significance indicates perfect model fitting. HL is calculated as follows [46]: G

HL HL = =

(y − ng Pg )c

G g

yg − ng Pg

∑ ng Pg (1 c− Pg )c

g 1g=1 ng Pg 1 − Pg

(3) (3)

where G G represents represents the where the number number of of groups groups (G (G ≤≤10). 10). nngg represents represents the the number number of of cases cases in in group group g. g. cgPgrepresents yg represents representsthe thenumber numberofofobserved observedevents eventsininit.it.P represents probability a predicted event y thethe probability of of a predicted event in g

c it. it. ng P represents the projections of the in ngg P the projections of event. the event. g represents 3. Results Results 3. 3.1. Industrial and Commercial Land Use Changes 3.1. Industrial and Commercial Land Use Changes As Figure 9 shows, from 2000 to 2010, the industrial and commercial land areas in the new scale As Figure 9 shows, from 2000 to 2010, the industrial and commercial land areas in the new scale were 14,174 and 4357 hectares, respectively. These spaces occupied up to 28.50% and 8.76% of the total were 14,174 and 4357 hectares, respectively. These spaces occupied up to 28.50% and 8.76% of the new construction land in the study area. Newly commercial land was mainly distributed around the total new construction land in the study area. Newly commercial land was mainly distributed around corecore area,area, and itand presented a balanced distribution. The newly industrial land was mainly distributed the it presented a balanced distribution. The newly industrial land was mainly in the north and the southeast of the study area, and it has a concentration distribution characteristic distributed in the north and the southeast of the study area, and it has a concentration distribution in those areas.in those areas. characteristic

Figure 9. 9. New New commercial commercial and and industrial industrial land land (2000–2010). (2000–2010). Figure

3.2. Estimation of Drivers of Land Use Change commercial land shows thatthat the the HL HL value waswas 0.2860.286 and The logistic logistic regression regressionmodel modelofofthe thenew new commercial land shows value is not significant (Table 2). The accuracy of the model was 70.30%. These two indicators demonstrate and is not significant (Table 2). The accuracy of the model was 70.30%. These two indicators that the model is athe good representation of the dependent and independent demonstrate that model is a good representation of the dependent and variables. independent variables. The logistic regression model of the new industrial land shows that the HL value of 0.216 is not significant. The accuracy of the model was 65.60%. These results also demonstrated that the model is a good representation of the dependent and independent independent variables. variables. 3.3. Determinants Determinants of of Industrial Industrial Land Land Change Change 3.3. Table 22 shows showsthe thedescriptive descriptive statistics of the explanatory variables. According to2,Table 2, Table statistics of the explanatory variables. According to Table factors factors that significantly contributed to industrial land growth in 2000–2010 were the number of that significantly contributed to industrial land growth in 2000–2010 were the number of enterprises enterprises services the area the in 2010, the agriculture and construction landinuses engaged in engaged services in (NEES) in (NEES) the areainin 2010, agriculture and construction land uses the in the neighborhood 2000planning and planning orders. Among hadlargest the largest impact on neighborhood in 2000inand orders. Among these,these, NEESNEES had the impact on the

development of industrial land with an odds ratio of 3.05, because service enterprises provide a favorable environment both for manufacturing activities in terms of producer services and for employers’ daily life in terms of consumer services. For example, Deng [47] completed an industrial


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the development of industrial land with an odds ratio of 3.05, because service enterprises provide aSustainability favorable2016, environment both for manufacturing activities in terms of producer services and for 8, 1323 12 of 18 employers’ daily life in terms of consumer services. For example, Deng [47] completed an industrial structure survey within within 11km kmofofthe theWanyuan Wanyuansubway subway station, located in the industrial park of structure survey station, located in the industrial park of the the Yizhuang economic development zone. The results showed that in 2010, the manufacturing Yizhuang economic development zone. The results showed that in 2010, the manufacturing industry industry of all industry in the area; however, there also anumber large number of existing and was 90% was of all90% industry in the area; however, there was alsowas a large of existing and under under construction services enterprises, which included three business centers, three finance services construction services enterprises, which included three business centers, three finance services institution The close close proximity proximity to to these institution and and one one hotel hotel (Figure (Figure 10). 10). The these services services was was cost cost effective effective and and helped manufacturers quickly connect to the market. Apart from the NEES, construction in the area helped manufacturers quickly connect to the market. Apart from the NEES, construction in the area also to the 2.15) for also contributed contributed to the growth growth of of industrial industrial land land (OR (OR = = 2.15) for aa similar similar reason, reason, but but with with wider wider implications implications regarding regarding residential, residential, transport transportand and public public facilities facilities and and other other urban urban functions. functions. The The odds odds ratio of the independent growth of industrial enterprises (2000–2010) was 1.69. This indicates that ratio of the independent growth of industrial enterprises (2000–2010) was 1.69. This indicates that if if the the number number of of industrial industrial units units increased increased by by one one percentage percentage point, point, the the possibility possibility for for future future increases increases in industrial units unitsincreases increases1.69-times. 1.69-times.The The odds ratio neighborhood agriculture in industrial odds ratio of of thethe neighborhood agriculture landland waswas 1.5, 1.5, which indicates that if the percentage of agriculture land in the neighborhood increased by one which indicates that if the percentage of agriculture land in the neighborhood increased by one unit, unit, the possibility for new increases in industrial land willincrease increase1.50-times. 1.50-times.This This reflects reflects urban the possibility for new increases in industrial land will urban expansion, especially the industrialization-driven suburbanization process, which encroached expansion, especially the industrialization-driven suburbanization process, which encroached upon upon aa huge odds ratio forfor planning orders waswas 1.55,1.55, which is greater than huge amount amountofofagricultural agriculturalland. land.The The odds ratio planning orders which is greater one that thethat amount of industrial land will land increase it is located the construction thanand oneindicates and indicates the amount of industrial willif increase if itwithin is located within the area defined by the city plan. construction area defined by the city plan.

(a)

(b)

Figure 10. 10. The within 11 km subway station. station. (a) (a) Libao Figure The commercial commercial infrastructure infrastructure within km of of Wanyuan Wanyuan subway Libao business business square (building in progress); (b) Agricultural Bank of China. square (building in progress); (b) Agricultural Bank of China.

Factors hampering land transfer to industrial uses included DEM (OR = 0), accessibility (0.12), Factors hampering land transfer to industrial uses included DEM (OR = 0), accessibility (0.12), population density in 2010 (0.3), water in the neighborhood (0.54), forest in the neighborhood (0.57) population density in 2010 (0.3), water in the neighborhood (0.54), forest in the neighborhood (0.57) and and growth of industrial enterprises (0.76). Because of the building restrictions in Beijing, it is growth of industrial enterprises (0.76). Because of the building restrictions in Beijing, it is impossible to impossible to build industrial land on a slope >10%, which was reflected by the DEM. Urban land build industrial land on a slope >10%, which was reflected by the DEM. Urban land developed during developed during 2000–2010 was primarily in the peri-urban area, with new construction land in this 2000–2010 was primarily in the peri-urban area, with new construction land in this area increasing area increasing to 35,764 ha, which made up 71% of new construction land in the study area. to 35,764 ha, which made up 71% of new construction land in the study area. However, Beijing is However, Beijing is a mono-centric city with better transportation conditions in the urban center than a mono-centric city with better transportation conditions in the urban center than other places. For cases other places. For cases where most industrial land was developed in the peri-urban area, the where most industrial land was developed in the peri-urban area, the accessibility factor was adversely accessibility factor was adversely associated with the growth of industrial land. The increase of associated with the growth of industrial land. The increase of relatively large industrial land areas was relatively large industrial land areas was also negatively related to population density. This creates also negatively related to population density. This creates an inconsistency between the distribution of an inconsistency between the distribution of large-scale residential areas and industrial areas, where large-scale residential areas and industrial areas, where there is a clear separation between the workers’ there is a clear separation between the workers’ residences and their workplaces. The odds ratios of residences and their workplaces. The odds ratios of forest land and bodies of water were 0.57 (1/1.76) forest land and bodies of water were 0.57 (1/1.76) and 0.54 (1/1.85), respectively. These values are both and 0.54 (1/1.85), respectively. These values are both less than one, which indicates that the probability less than one, which indicates that the probability of industrial growth in forest or water areas was low; specifically, the probability of industrial growth decreased by 1.76- or 1.85-times as forest or water area increased by 1%.


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of industrial growth in forest or water areas was low; specifically, the probability of industrial growth decreased by 1.76- or 1.85-times as forest or water area increased by 1%. 3.4. Determinants of Commercial Land Change Factors contributing to land transferred for service use included the NEES (OR = 1901), construction in the neighborhood (2.94) and accessibility improvement (1.94). For example, newly commercial land in Huilonguan in Changping increased by 83 ha, which was higher than the average increase by 44.46 ha in our study area. In 2000, the percentage of construction density was 70.49%, higher than the average level of 41.61% in our study area. The services enterprise density in 2000 was 1790.39 per km2 , much higher than the average level of 861 per km2 . Within 2000–2010, Subway Line 13 was built in Huilonguan, and then, the transport accessibility was substantially improved with an improvement of 15 min [47]. The odds ratio of population density was one, which indicated that the current permanent resident population had no significant influence on new commercial land. The main reason is that Beijing has a strategy guiding the population distribution for industry development, which is to first develop commercial land, then guide commercial enterprises into these areas, after which the population of the area would occur. Therefore, population increases occurred after commercial land development, and there was no significant relationship between population and new growth of commercial land. There were also a number of factors counteracting the growth of commercial land. The odds ratios for the number of industrial enterprises and its growth were 0.38 and 0.29, respectively, implying that the probability of the area for service use would reduce by 62% and 71% if the number of industrial enterprises was larger or increased by one unit. During 2000–2010, Beijing implemented the strategy of “suppress the second industry and develop the third industry”, which had a considerable effect on the city’s spatial structure. It is also reflected in the movement of many industrial enterprises to outer suburban districts while the tertiary industry activities, especially the commercial activities, continuously increased in the function extension area. According to the enterprise statistics, within the third ring road, the industrial enterprise density had decreased by 1.66 per km2 , whereas within the third ring road and sixth ring road, the service enterprises increased by 526.25 per km2 . Commercial land expanded mostly in the peri-urban area; the factors impacting the growth of commercial land and accessibility in 2010 were negatively associated. According to the economic census data of Beijing, the added value of services in Beijing increased from 942 billion Yuan in 2000 to 5577 billion Yuan in 2010, primarily in the regions of Chaoyang, Fengtai, Shijinshan and Haidian from 94.18 billion Yuan in 2000 to 557.73 in 2010. Because of the restrictions on planning orders in Beijing, it was impossible to build on industrial land with a slope >10%, which was reflected by the DEM. 4. Discussion 4.1. Comparison of the Determinants Comparing the determinants of manufacturing and commercial land growth is necessary, as the similarities and differences among the drivers of industrial and commercial land transfer can shed light on possible patterns and underlying mechanisms of urban development. 4.1.1. Socio and Economic Factors The growth of industrial enterprises was positively correlated with industrial land use increase. However, both the growth of industrial enterprises and the number of industrial enterprises correlated negatively with commercial land increases, and the odds ratios were 0.29 (1/3.43) and 0.38 (1/2.66), respectively. This indicated that reducing the number of industrial enterprises by one unit would increase the commercial land use by about three times. The main reason for this


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was that, during 2000–2010, when the speed of city updating was faster than the last few decades, much industrial land was turned into commercial land, and much of the other land was also changed to commercial land. According to economic census data from 2000 to 2010, the production value of tertiary industry increased by 23% and 22% in core and function extension areas, respectively, and these are important areas of tertiary industry. Meanwhile, the production value of secondary industry decreased from 47.5% down to 33.8% [47]. The NEES correlated positively with both industrial and commercial land increase, with odds ratios of 3.05 and 1900.74, respectively, which indicated that service enterprises had strong attraction to all kinds of enterprises. For example, the industry park of Zhongguancun was not only a gathering place for new high-tech industrial enterprises, but also for services such as financial, commercial and catering enterprises. Moreover, an economic census showed that the service enterprise density within newly commercial land was 1186.86 per km2 in 2010 and much higher than the average value (1015.77 per km2 ) of the Zhongguancun industrial park. The current permanent resident population had a negative correlation with increasing industrial land, whereas it had a positive correlation with increasing commercial land. However, the relationship between population and increasing commercial land was not significant because the odd ratio is 1.00. Therefore, increasing industrial land occurred in areas with low population density, creating a separation between workers’ residences and workplaces in industrial areas. For example, the worker’s average commuting time in the Yizhuang economic development zone as measured by Meng [48] was 37.3 min. However, increases of industrial land use are now approaching areas with high population density. According to the population statistics of the study area in 2010, the average population density within new commercial land was 105,269.90 per km2 , which is much higher than the average 89,122.04 per km2 of the whole study area. 4.1.2. Built Environment Changing and improved accessibility can significantly contribute to developing commercial land, but not to industrial land. This is perhaps because industrial parks dominate industrial land development in Beijing [24] and are usually located in peri-urban or remote areas that are cost-effective, but have less favorable accessibility. Conversely, services enterprises usually tend to pay relatively higher rental changes where they possess advantageous location and transport conditions. For example, Yao and Xiong [49] studied the impact of urban mass transit on land use. The results show that the proportion of commercial and financial building areas is much higher closer to subway stations, and this proportion is 40% within the 250-m range. Land development has three kinds of effects on the areas where it occurs. Construction in the neighborhood exerts a similar effect in attracting industrial and commercial land development (OR = 2.15 and 2.54, respectively). The slightly lower odds for industrial land reflects the fact that industrial development has more flexible conditions than the location of commercial land. Agriculture in the area is positively linked to industrial land growth, but was not significant for commercial land. This reflects that industrialization processes penetrate into the peri-urban and rural areas, but this is not paralleled by services activities. Restrictive factors, including forest and water, played a role in prohibiting industrial land development, but were not significant for commercial land. 4.1.3. Nature Factor The landform was the most significant restraining factor for these two types of land development because of development costs and building restrictions. According to the requirement of The Beijing City Master Plan, for construction in the city land must have a gradient below 10% [40]. For example, Changping district is located in the zone of transition from plains to mountains, with significant topographical differences. In this district, the area of construction land below the gradient limit is 21,118.44 ha, or 98.79% of the total construction land.


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4.1.4. Urban Planning Urban planning is one of the most important institutional factors influencing land development. Making the right decision regarding the function of each parcel of land is the primary task of urban planners. However, our model indicates that planning orders are only significantly reflected by industrial land development at an OR of 1.547, owing to the industrial park-dominated policy in Beijing [26]. This inconsistency could be due to the high speed and large volume of urban development in Beijing during 2000–2010. In 2010, the area of construction land was 183,865.70 ha, which far exceeded the planned construction land area of 107,331.50 ha in 2020. 4.2. Recommendations of Urban Sustainable Development Land development is an important issue for the economic sustainability of the city; particularly, it requires vast infrastructure investment and construction. In addition, the uses of land are compatible with one another to some extent. Significantly affected by market forces, planned orders may not often work well, especially when planning for commercial activities. Hence, in order to justify urban infrastructure investment and effectively make use of land, planners should respect market forces and more specifically the different effects of various factors on manufacturing and commercial land use development. Our analysis indicates that the area of manufacturing enterprises does not favor of commercial use, but land in commercial use can encourage manufacturing use for the land nearby. The analysis further indicates there are different associations for commercial and manufacturing uses of land with the improvement of transport accessibility. Therefore, the improvement of transport accessibility could contribute to the realization of Beijing transferring from an industrial city into a post-industrial and modern city, which is labeled by the prosperity of services. Compared to previous literature, our research suggests that in order to achieve economic sustainability in land development, planners should be careful about different effects of various potential factors, as examined in our research, on manufacturing and service use of land. 5. Conclusions Using a logistic model, we conducted an empirical study of urban land development for manufacturing and services use from 2000 to 2010, primarily focused on the determinants of industrial and commercial land growth from 2000 to 2010. The following are our six main conclusions: (1)

(2)

(3)

(4)

Over the period of 2000–2010, industrial land and commercial land in the study area grew on a large scale; by 28.50% and 8.76% of the total new construction land in the study area, respectively. New commercial land was mainly distributed around the core area and was present with a balanced distribution characteristic. New industrial land was concentrated in sub-districts. The number of enterprises engaged in services (NEES) in 2010 in the locale, agriculture and construction land uses in the neighborhood in 2000 and planning orders significantly contributed to newly-added industrial land during 2000–2010. Among these factors, NEES exerted the largest effect on the occurrence of industrial land. Factors hampering land transfer to industrial uses included DEM, accessibility, population density in 2010, the presence of water or forest and the growth of industrial enterprises. Factors contributing to land transfer for services/commercial use included the NEES with very high odds ratios, construction in the neighborhood and accessibility improvement. However, the current permanent resident population has no significant influence on the increase of commercial land. The number of industrial enterprises and their growth were factors that counteracted the growth of commercial land. New commercial land expanded mostly in peri-urban areas, and accessibility in 2010 was negatively associated. Urban land use change is driven by social and economic development. During 2000–2010, the city experienced fast growth, as a large amount of industrial land turned into commercial land, in turn


(5)

(6)

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creating some new commercial growth. The number of service enterprises correlated positively with both industrial and commercial land increases, which indicated that service enterprises had a strong attraction to all kinds of enterprises. Increases in industrial land occurred in areas with low population density; while commercial land showed the opposite form of development. Moreover, the phenomenon of the separation of workplace and residence was more obvious in industrial areas. Environment is a major factor in urban land use change. Improved accessibility can significantly contribute to the development of commercial land, but not to industrial land. Construction in the neighborhood exerts a similar effect of attracting industrial and commercial land development. Agriculture in the neighborhood is positively linked to the industrial land growth, but not to that of commercial land. Restrictive factors, including the presence of forest and bodies of water, play a role in prohibiting industrial land development, but are not significant for commercial land. Regarding the nature factor, landform is the most significant restraining factor for these two types of land developments because of development costs and building restrictions. With regard to institutional factors, urban planning is one of the most important factors influencing land development. However, the model indicates that planning orders are only significant for industrial land development, owing to an industrial parks-dominated policy in Beijing. Conversely, it is not significant for commercial land uses.

Supplementary Materials: The following are available online at www.mdpi.com/2071-1050/8/12/1323/s1, Table S1: All indicators selected in the article, Table S2: Result of the correlation analysis of the population indicators, Table S3: Results of the correlation analysis of economic and employment indicators. Acknowledgments: This research is sponsored by a grant of the Natural Sciences Foundation of China (No. 41371008, 41530751, 41601575 and 41601164), the Early Career Talent Program of Chinese Academy of Sciences ‘Youth Innovation Promotion Association of Chinese Academy of Sciences’ (2014042), the Innovative Think-tank Foundation for Young Scientists of China Association for Science and Technology (DXB-ZKQN-2016-010), the Opening fund of the Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education (201701) and the China Scholarship Council (201504910654). Finally, we are grateful for the land use data support of the Beijing Municipal Bureau of Land and Resources. Author Contributions: Zhenshan Yang, Yu Deng and Chuanzhun Sun jointly conceived of and designed the framework. Zhenshan Yang, Chuanzhun Sun and Chao Sun interpreted that data and wrote the paper. Chao Sun and Chuanzhun Sun conducted the data collection and analysis. Jikang Zhang made contributions to the data analysis and discussions. Conflicts of Interest: The authors declare no conflict of interest.

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